Robust Measure Research Articles

Renewable energy planning in Africa: robustness of mean and extreme multi-model climate change patterns in solar PV and wind energy potentials

Increasing renewable sources in the energy mix is essential to mitigate climate change, not least in countries where the energy demand is likely to rise over the coming decades to reduce or even skip durations of time where fossils dominate. For Africa, solar photovoltaic (PV) and inland wind energy, combined with hydropower, provide significant and untapped potentials, whereas trends and robustness measures need further investigation. This study aims to gain insight into distributed trends in solar PV and wind energy potentials over Africa. This study employs relevant metrics, including relative change, model agreement, robustness, bias, and absolute levels for every available model combination and two climate scenarios, with energy planning purposes in mind. The study finds that regional climate models were the primary control of spatio-temporal patterns over their driving global climate model. Solar PV potentials show more coherence between models, a lower bias and general high potentials in most African regions than wind potentials. Favourable locations for inland wind energy include mainly the regions of greater Sahara and the Horn region. For wind and solar potentials combined, scattered locations within Sahara stand out as the most favourable across scenarios and periods. The analysis of minimum energy potentials shows stable conditions despite low potentials in certain regions. The results demonstrate a potential for solar and wind power in most of the African regions and highlight why solar and wind power or synergies of energy mix should be considered for local energy planning and storage solutions.

Total mass slopes and enclosed mass constrained by globular cluster system dynamics

We study the total-mass density profiles of early-type galaxies (ETGs: ellipticals and lenticulars) with globular clusters (GCs) as kinematic tracers. The goal of this work is to probe the total mass distribution, parameterised with a double power-law profile, by constraining the parameters of the profile with a flexible modelling approach. To that end, we leverage the extended spatial distribution of GCs from the SLUGGS survey (⟨RGC, max⟩∼8 Re) in combination with discrete dynamical modelling. We use discrete Jeans anisotropic modelling in cylindrical coordinates to determine the velocity moments at the location of the GCs in our sample. Assuming a Gaussian line-of-sight velocity distribution (LOSVD) and a combination of informative and uninformative priors we use a Bayesian framework to determine the best-fit parameters of the total mass density profile and orbital properties of the GCs. We find that the choice of informative priors does not impact the enclosed mass and inner slope measurements. Additionally, the orbital properties (anisotropy and rotation of the dispersion-dominated GC systems) minimally impact the measurements of the inner slope and enclosed mass. A strong presence of dynamically-distinct subpopulations or low numbers of kinematic tracers can bias the results. Owing to the large spatial extent of the tracers our method is sensitive to the intrinsic inner slope of the total mass profile and we find ᾱ = −1.88 ± 0.01 for 12 galaxies with robust measurements. To compare our results with literature values we fit a single power-law profile to the resulting total mass density. In the radial range 0.1–4 Re our measured slope has a value of ⟨γtot⟩= − 2.22 ± 0.14 and is in good agreement with the literature. Due to the increased flexibility in our modelling approach, our measurements exhibit larger uncertainties, thereby limiting our ability to constrain the intrinsic scatter σγ.

Assessing User Perceptions and Realities in FinTech Application Performance: A Comparative Analysis

Financial services have been fundamentally transformed by FinTech, an innovation in technology that has caused significant shifts in the financial sector. This study delves into the impact of two prominent FinTech platforms, Stripe and Square, on the banking industry. Stripe specializes in online payments, customization, and agility, with a focus on in-person transactions, catering to technologically adept startups. Conversely, Square offers tools tailored for small retail and restaurant enterprises. The study aims to bridge the gap between user perceptions and actual performance, focusing on security and reliability concerns. Despite the convenience offered by FinTech, concerns over security persist, highlighting the need for improved user confidence through robust security standards and transparency. The research explores the historical evolution of FinTech, emphasizing the integration of advanced technologies like AI and blockchain. The research analyzes FinTech performance evaluation methodologies, including key performance indicators (KPIs), to showcase the significance of assessing financial success, customer satisfaction, and user experience. The study outlines the criteria for selecting Stripe and Square, considering industry prominence, market share, a diverse user base, service offerings, innovation, accessibility, and financial inclusion initiatives. The methodology employs a qualitative approach to systematically analyze the performance and user experience of both platforms. Features, pricing, target audience, security, and user experience influence user preferences, according to key findings. The comparative analysis between Stripe and Square indicates a market trend where businesses choose platforms based on operational nature, settlement speed, and specific financial needs. The study underscores the transformative role of FinTech within the financial sector, driven by technological innovation, financial inclusion initiatives, diverse user bases, user-centric design, and robust security measures. The implications suggest a future FinTech landscape characterized by continued emphasis on innovation, inclusivity, user-centric approaches, and security. The research concludes by highlighting the significant impact of Stripe and Square on the financial sector and providing insights for stakeholders to adapt strategies in the dynamic FinTech landscape. Further research opportunities lie in exploring evolving user preferences, emerging technologies, and regulatory developments shaping the future of FinTech.

Comparison of RT-ddPCR and RT-qPCR platforms for SARS-CoV-2 detection: Implications for future outbreaks of infectious diseases

The increased frequency of human infectious disease outbreaks caused by RNA viruses worldwide in recent years calls for enhanced public health surveillance for better future preparedness. Wastewater-based epidemiology (WBE) is emerging as a valuable epidemiological tool for providing timely population-wide surveillance for disease prevention and response complementary to the current clinical surveillance system. Here, we compared the analytical performance and practical applications between predominant molecular detection methods of RT-qPCR and RT-ddPCR on SARS-CoV-2 detection in wastewater surveillance. When pure viral RNA was tested, RT-ddPCR exhibited superior quantification accuracy at higher concentration levels and achieved more sensitive detection with reduced variation at low concentration levels. Furthermore, RT-ddPCR consistently demonstrated more robust and accurate measurement either in the background of the wastewater matrix or with the presence of mismatches in the target regions of the consensus assay. Additionally, by detecting mock variant RNA samples, we found that RT-ddPCR outperformed RT-qPCR in virus genotyping by targeting specific loci with signature mutations in allele-specific (AS) assays, especially at low levels of allele frequencies and concentrations, which increased the possibility for sensitive low-prevalence variant detection in the population. Our study provides insights for detection method selection in the WBE applications for future infectious disease outbreaks.

Phenol is a pH-activated linker to gold: a single molecule conductance study.

Single molecule conductance measurements typically rely on functional linker groups to anchor the molecule to the conductive electrodes through a donor-acceptor or covalent bond. While many linking moieties, such as thiols, amines, thiothers and phosphines have been used among others, very few involve oxygen binding directly to gold electrodes. Here, we report successful single molecule conductance measurements using hydroxy (OH)-containing phenol linkers and show that the molecule-gold attachment and electron transport are mediated by a direct O-Au bond. We find that deprotonation of the hydroxy moiety is necessary for metal-molecule binding to proceed, so that junction formation can be activated through pH control. Electronic structure and DFT+Σ transport calculations confirm our experimental findings that phenolate-terminated alkanes can anchor on the gold and show charge transport trends consistent with prior observations of alkane conductance with other linker groups. Critically, the deprotonated O--Au binding shows features similar to the thiolate-Au bond, but without the junction disruption caused by intercalation of sulfur into electrode tips often observed with thiol-terminated molecules. By comparing the conductance and binding features of O-Au and S-Au bonds, this study provides insight into the aspects of Au-linker bonding that promote reproducible and robust single molecule junction measurements.

Challenges of Non-STEM Students in Psychology: Disruptive Learning Dynamics: A Literature Review

This research meticulously explores the complex terrain of disruptive learning encounters among non-STEM psychology students at Father Saturnino Urios University (FSUU), aiming to unravel the intricate moderating factors that shape the relationship between disrupted learning progression and psychological challenges. Situated within the framework of the K to 12 Program, which introduced diversified tracks in secondary education, students were empowered to align their academic pursuits with their unique aptitudes and career aspirations. Nevertheless, a pronounced misalignment between senior high school strands and subsequent college courses, particularly within STEM fields, has emerged as a notable concern. Guided by the theoretical underpinning of the vertical alignment theory, this study posits that the misalignment between high school strands and college courses not only adversely impacts academic performance but also engenders psychological challenges. Embracing an Exploratory Sequential Mixed Method Design, the research seamlessly integrates qualitative and quantitative methodologies, commencing with a qualitative exploration to inform subsequent robust quantitative measures. The findings of this study hold significance in unveiling nuanced factors underpinning disruptive learning, with the potential to inform educational policies and practices, fostering inclusive experiences for diverse student cohorts.

Disruption of the Covid-19 pandemic on the agri-food sector: A systematic review of its implications in post-pandemic and future of food security

<abstract> <p>The unprecedented challenges posed by the global COVID-19 pandemic have significantly impacted various sectors, with the agri-food system proving especially vulnerable to disruptions. Ensuring food security and bolstering the resilience of agri-food systems in the aftermath of the pandemic has emerged as a crucial concern. Utilizing the PRISMA protocol, this systematic literature review systematically searched relevant databases and repositories using specific keywords related to agri-food security in the pre and post-pandemic context. The final review incorporated 37 articles. The results revealed that the pandemic profoundly impacted all facets of the agri-food sector, particularly agri-food systems. Despite this, we identified several measures that were implemented or recommended to mitigate disruptions caused by the pandemic and ensure the resilience of the agri-food sector for future similar events. Robust policy measures, promotion of urban agriculture, support for small-scale farmers, enhancement of the international market, technological innovations, collaborations, research and development, and the promotion of novel foods were all highlighted as crucial resilience measures. Policymakers, researchers, and stakeholders are urged to adopt a comprehensive approach encompassing these measures to build a resilient and secure food future in the post-pandemic era.</p> </abstract>

Multifaceted luminescence in lanthanide-activated microrods for advanced multicolor anti-counterfeiting

The increasing demand for robust anti-counterfeiting measures has prompted the development of advanced luminescent materials that offer multifaceted capabilities for secure information encryption and authentication.

Secure-Tech Triad Enhancing Electronic Voting System Security through Integrated Blockchain, AI, and IoT Technologies

As electronic voting systems become increasingly prevalent, the urgent need for robust security measures to combat evolving cyber threats has never been more critical. This paper introduces a ground-breaking architectural framework Secure-Tech Triad that synergistically combines Blockchain technology with Machine Learning (ML) algorithms and Internet of Things (IoT) capabilities to enhance the security and efficiency of electronic voting systems. This architectural framework utilizes a modified Proof-of-Stake (PoS) Blockchain algorithm, a Random Forest ML model for real-time anomaly detection, and an MQTT protocol for IoT-based data collection to create a more secure, efficient, and responsive voting environment. Rigorous testing and evaluation show that the integrated framework significantly outperforms existing Blockchain-only solutions in key performance indicators, such as security breach detection rate, system latency, and cost efficiency. This integrated approach is the best-performing model, achieving a 97% security breach detection rate, a 30% reduction in system latency (down to 2.3 seconds), and a 25% decrease in operational costs. These results underscore the combined effectiveness of Blockchain, AI, and IoT in enhancing security, speed, and cost-effectiveness. Specifically, the Random Forest algorithm has been instrumental in achieving an exceptional security breach detection rate, while IoT data collection has played a pivotal role in enabling real-time anomaly detection and proactive threat mitigation.

Robust measurement of orbital angular momentum of a partially coherent vortex beam under amplitude and phase perturbations

Robust measurement of orbital angular momentum of a partially coherent vortex beam under amplitude and phase perturbations

Performance Analysis of Distributed Database Management System for IoT in the Context of Smart Agriculture in Indonesia

The integration of Distributed Database Management Systems (DDBMS) for Internet of Things (IoT) applications in smart agriculture presents a transformative paradigm in the dynamic landscape of Indonesian agriculture. This qualitative research investigates the challenges and opportunities associated with this integration, drawing insights from interviews with diverse stakeholders, including farmers, technology developers, and policymakers. The technical challenges encompass data synchronization, network latency, and security considerations, emphasizing the need for standardized protocols and robust security measures. Infrastructure challenges, such as connectivity issues and power supply dependency, underscore the importance of rural infrastructure development. User acceptance challenges highlight resistance to change and the perceived complexity of DDBMS, emphasizing the significance of targeted awareness and training programs. Despite these challenges, stakeholders recognize the advantages of scalability, enhanced data availability, and improved decision-making capabilities offered by DDBMS. The study proposes recommendations, including technological solutions, policy interventions, and capacity-building initiatives, to address identified challenges and promote the effective utilization of DDBMS in Indonesian smart agriculture. This research contributes context-specific insights to the global discourse on IoT and DDBMS in agriculture.

Exploring Privacy-Preserving Disease Diagnosis: A Comparative Analysis

In the healthcare sector, data is considered as a valuable asset, with enormous amounts generated in the form of patient records and disease-related information. Leveraging machine learning techniques enables the analysis of extensive datasets, unveiling hidden patterns in diseases, facilitating personalized treatments, and forecasting potential health issues. However, the flourish of online diagnosis and prediction still faces some challenges related to information security and privacy as disease diagnosis technologies utilizes a lot of clinical records and sensitive patient data. Hence, it becomes imperative to prioritize the development of innovative methodologies that not only advance the accuracy and efficiency of disease prediction but also ensure the highest standards of privacy protection. This requires collaborative efforts between researchers, healthcare practitioners, and policymakers to establish a comprehensive framework that addresses the evolving landscape of healthcare data while safeguarding individual privacy. Addressing this constraint, numerous researchers integrate privacy preservation measures with disease prediction techniques to develop a system capable of diagnosing diseases without compromising the confidentiality of sensitive information. The survey paper conducts a comparative analysis of privacy-preserving techniques employed in disease diagnosis and prediction. It explores existing methodologies across various domains, assessing their efficacy and trade-offs in maintaining data confidentiality while optimizing diagnostic accuracy. The review highlights the need for robust privacy measures in disease prediction, shortcomings related to existing techniques of privacy preserving disease diagnosis, and provides insights into promising directions for future research in this critical intersection of healthcare and privacy preservation.

Healthcare cybersecurity and cybercrime supply chain risk management

Cybersecurity is paramount in today’s rapidly evolving healthcare industry, particularly as supply chain management and logistics undergo digital transformation. This study examines the substantial threat posed by cybercrime to patient safety, data security, and operational efficiency within healthcare logistics and supply chain management. These risks can significantly impact an organization’s reputation and financial stability, necessitating vigilant detection and mitigation efforts by healthcare companies. As the primary defence against online threats, cybersecurity plays a pivotal role in preventing data breaches, cyberattacks, and other malicious activities that could have devastating consequences for the healthcare sector. Its core objective is to ensure the availability, confidentiality, and integrity of data, systems, and resources within the realm of healthcare supply chain management and logistics. Patient data protection stands out as a critical aspect of cybersecurity in this context. Healthcare logistics and supply chain management systems frequently handle sensitive patient data, encompassing billing details and medical histories. The compromise of such data places patient trust and the organization’s regulatory compliance at risk, potentially leading to identity theft, fraudulent claims, and privacy breaches. Furthermore, safeguarding the security of medical equipment is of paramount importance. With the increasing connectivity of these devices through the Internet of Things (IoT), they become more vulnerable to cyberattacks. Apart from jeopardizing patient safety, a breach in medical device security raises questions about the authenticity and reliability of healthcare products and services. Another pressing issue that healthcare institutions must address is unauthorised access to their systems. Cybercriminals persistently seek entry points into these systems to exploit vulnerabilities for illicit or profitable purposes. Robust cybersecurity measures are essential to thwarting unauthorised access and ensuring that only authorised individuals can access and modify sensitive medical data. Maintaining the accuracy of patient records is crucial for efficient supply chain management and healthcare logistics. Cyberattacks that manipulate or corrupt patient records can lead to medical errors, endangering patient safety. Consequently, cybersecurity measures must include safeguards to preserve the integrity and accuracy of these records. Beyond these immediate concerns, cybersecurity is instrumental in preventing disruptions to healthcare operations and services. Downtime resulting from cyberattacks can be catastrophic, impeding patient care and undermining the overall effectiveness of supply chain management and healthcare logistics. Cybersecurity safeguards continuous healthcare services by guaranteeing the security and accessibility of data and systems. Furthermore, cybersecurity plays a pivotal role in protecting the integrity, trust, and reputation of healthcare organizations. A cyberattack or data breach can tarnish an organization’s reputation and erode patient confidence. Such damage can have enduring repercussions, affecting an organization’s ability to attract clients, partners, and investors.

Review of strategic alignment: Accounting and cybersecurity for data confidentiality and financial security

In the contemporary landscape of rapidly evolving technological advancements and the increasing prevalence of cyber threats, organizations face a critical imperative to align their accounting practices with robust cybersecurity measures. This review explores the symbiotic relationship between accounting and cybersecurity in safeguarding data confidentiality and ensuring financial security. Focusing on the intersection of these two domains, we examine the strategic alignment required to fortify organizations against the escalating challenges posed by cyber threats to sensitive financial information. The review begins by delving into the intricate connection between accounting processes and the protection of financial data, emphasizing the pivotal role of accurate financial reporting and transparent disclosure in maintaining stakeholder trust. Subsequently, it scrutinizes the evolving threat landscape, identifying cyber risks that specifically target financial systems and data. The analysis underscores the need for a comprehensive strategic approach that integrates accounting practices with cybersecurity protocols to effectively mitigate these risks. Furthermore, the review investigates the contemporary tools and technologies that facilitate the integration of accounting and cybersecurity, enhancing organizations' ability to detect, prevent, and respond to cyber threats. It explores the adoption of advanced encryption methods, intrusion detection systems, and artificial intelligence-driven analytics to bolster data confidentiality and financial security. In examining case studies and best practices, this review highlights successful instances of organizations aligning accounting and cybersecurity strategies to achieve a cohesive defense against financial cyber threats. Lessons learned from these cases offer valuable insights for practitioners and decision-makers seeking to implement effective measures within their own organizational contexts. Ultimately, this review contributes to the evolving discourse on strategic alignment by emphasizing the imperative of synergizing accounting practices with cybersecurity initiatives. As organizations navigate an increasingly complex and interconnected business environment, a holistic approach that unifies financial integrity and cyber resilience becomes paramount for ensuring sustained success and safeguarding against the multifaceted challenges of the digital age.

Navigating the path to economic empowerment: overcoming challenges in digital platforms for economic empowerment

This paper presents a comprehensive survey of the challenges faced by economic empowerment digital platforms. As these online platforms revolutionize the economic landscape, they encounter various hurdles that hinder their potential for fostering financial inclusion and empowerment. Through an extensive analysis of existing literature and empirical studies, this survey identifies and categorizes the key challenges faced by these platforms. The survey reveals that regulatory frameworks pose significant obstacles to economic empowerment digital platforms. Issues such as unclear or outdated regulations, varying legal requirements across jurisdictions, and the need for compliance with consumer protection and data privacy laws hinder platform operations and expansion. Moreover, the absence of standardized regulations often leads to legal uncertainties and challenges in ensuring fair competition. Furthermore, the survey highlights the persistent challenge of trust and reputation management within economic empowerment digital platforms. As these platforms rely heavily on user-generated content and interactions, establishing trust among users becomes critical. Issues such as fraudulent activities, identity verification, and dispute resolution mechanisms emerge as key challenges that platforms must address to maintain user trust and credibility. The survey also addresses the technological challenges faced by economic empowerment digital platforms. Issues such as cybersecurity threats, data breaches, and privacy concerns pose significant risks to both platform operators and users. Platforms must invest in robust security measures and adopt innovative technologies to mitigate these challenges and safeguard user information and transactions. Additionally, the survey examines challenges related to financial inclusion and accessibility. Despite the potential for economic empowerment, certain groups, such as individuals from low-income communities or those lacking digital literacy, may face barriers in accessing and utilizing these platforms. Bridging the digital divide and ensuring equal opportunities for all individuals to participate and benefit from these platforms remains an ongoing challenge. Finally, the survey highlights the need for collaboration and cooperation among stakeholders. Platform operators, policymakers, regulatory bodies, and user communities must engage in constructive dialogue to address the challenges faced by economic empowerment digital platforms effectively. Collaborative efforts can lead to the development of regulatory frameworks, industry standards, and best practices that foster a conducive environment for these platforms to thrive.

Enhancing Security of 5G-Enabled IoT Systems through Advanced Authentication Mechanisms: A Multifaceted Approach

The Internet of Things (IoT) has revolutionized communication among devices, offering unprecedented efficiency and convenience. However, the widespread adoption of IoT has raised significant security concerns, emphasizing the need for robust security measures. This study focuses on the crucial aspect of authentication within the layered architecture of IoT systems. Authentication is foundational to the architecture, ensuring the availability, security, and integrity of IoT services and data. The research evaluates the current state of IoT authentication techniques, highlighting limitations in conventional solutions. To address these shortcomings, an advanced authentication framework is proposed, incorporating cutting-edge technologies such as blockchain, artificial intelligence, and biometrics. The framework employs biometric data for a dynamic and adaptive authentication process, enhancing security and accuracy in user and device identification. Blockchain technology is integrated to establish a decentralized and tamper-resistant identity management system, reducing the risk of unauthorized access and data manipulation. Artificial intelligence continuously adapts authentication processes based on behavioural patterns, bolstering the system's resilience against evolving cyber threats. The study also discusses the practical application of the proposed authentication system, considering resource limitations in IoT devices. It provides insights into the efficiency, scalability, and interoperability of the suggested solution within various IoT ecosystems. The research contributes to the ongoing discourse on IoT security by thoroughly examining enhanced authentication procedures. Organizations can fortify their IoT deployments against a growing array of cyber threats by prioritizing advanced authentication within a layered design, fostering a more secure and reliable IoT ecosystem. Additionally, the study presents a comprehensive overview of the state-of-the-art security in IoT, exploring various designs, enabling technologies, and protocols. It delves into security challenges at each architectural tier, providing an in-depth analysis of attack taxonomies and advanced defenses. The article serves as a valuable resource for researchers and academics in the IoT sector, offering a detailed survey of architectural security, identification of challenges, resolution strategies, and insights into the evolving landscape of IoT. The study provides a comprehensive survey of IoT architectural security, identifying challenges, proposing resolutions, and highlighting changes in the IoT domain. This research aims to enhance the accuracy by 80% of IoT security, fostering a more secure and reliable IoT ecosystem.

The Impact of Flocculation on In Situ and Ex Situ Particle Size Measurements by Laser Diffraction

AbstractAccurate particle size distribution (PSD) measurements of suspended particulate matter (SPM) composed of flocs and aggregates are important to improve understanding of ecological and geomorphological processes, and for environmental engineering applications. PSDs can be measured in situ (in the field) using a submersible sensor, or ex situ (in the laboratory) using samples. The methodological choice is often guided by logistical factors, and the differences in PSDs acquired by in situ and ex situ measurements is of concern. In this study, a laser‐diffraction instrument (the LISST‐200X) was used to compare in situ and ex situ PSD measurements. Samples measured ex situ were stored for three consecutive weeks and measured each week in a laboratory using different stirrer speeds. We observed that ex situ measurements display a higher D50 (median particle size) than in situ measurements of the same sample (up to 613% larger, 112% on average). Our experiments show that the difference between in situ and ex situ measurements can be explained by flocculation of the riverine sediments during the first week of storage. During the subsequent ex situ measurements, the stirring results in a significantly lower D50. Ex situ measurements are therefore unsuitable for flocculated SPM. This study provides recommendations for optimizing PSD measurements by calculating the measurement times required to obtain robust PSD measurements (exceeding 3 min per sample), which are larger for field samples with coarser particles and wider PSDs.

Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review

Objectives: This article reviews and provides an exhaustive examination of task offloading and trajectory control techniques in unmanned aerial vehicles (UAVs) integrated with Internet of Things (IoT), highlighting their significance and impact on the UAV ecosystem. The paper begins by introducing the fundamental concepts of UAVs, IoT, and their integration, emphasizing the potential benefits and challenges of this union. Subsequently, it delves into an extensive exploration of task offloading, a critical aspect that optimizes UAV operations by distributing tasks between the UAV and edge/cloud computing resources. Various task offloading strategies, including computation offloading, data offloading, and control offloading, is discussed in detail, elucidating their role in optimizing resource utilization, energy efficiency, and real-time decision-making in UAVs.Methods: The review comprehensively covers trajectory control techniques, which are essential for ensuring UAVs can navigate through dynamic environments safely and efficiently. This study outlines the use of IoT technologies, such as GPS, sensors, and communication networks, to enable precise trajectory planning, obstacle avoidance, and adaptive path adjustments. It also discusses the integration of machine learning and AI algorithms for autonomous UAV navigation, taking into account environmental factors, mission objectives, and real-time data from IoT sources. The paper further discusses the challenges and potential security concerns associated with IoT integration in UAVs, as well as the emerging trends and future prospects of this dynamic field. It emphasizes the need for standardized protocols and robust cybersecurity measures to ensure the reliability and safety of UAV-IoT systems.Findings: This exhaustive review offers a comprehensive understanding of the synergistic relationship between UAVs and IoT, shedding light on the task offloading and trajectory control techniques that empower these autonomous aerial vehicles. By leveraging IoT technologies, UAVs are poised to continue transforming industries and driving innovation in ways previously unimaginable, making this interdisciplinary field an area of great promise and significance.

Psychometric properties of the Mexican version of the Intolerance of Uncertainty Scale: The IUS-12M

Background: The Intolerance of Uncertainty Scale short version (IUS-12) has proven to be a robust self-report measure to assess intolerance of uncertainty. While previous psychometric analyses of the IUS-12 have established a stable two-factor structure corresponding to the prospective and inhibitory factors of intolerance of uncertainty, recent studies suggest that the bifactor model may better explain its factor structure. Objective: The current study aimed to adapt and validate the IUS-12 in a Mexican population culturally. Method: The study consisted of a convenience non-probabilistic community sample of 405 adults between 18 and 70 years. Results: Confirmatory factor analyses supported a bifactor model and good internal consistency. Invariance testing indicated partial invariance across women and men. Convergent validity tests showed that the IUS-12 was related to measures of worry, as well as depression and anxiety. Conclusion: These findings provide evidence for the reliability and validity of the adapted version of the IUS-12 in Mexico.

Assessment of Riverbank Erosion and Accretion and its Impact on the People of Chairman Ghat, Noakhali, Bangladesh

Nestled within the vastness of the Meghna deltaic plain, Noakhali, and in particular, Chairman Ghat, stand as poignant examples of Bangladesh's intricate relationship with environmental challenges. The significance of the Meghna deltaic plain cannot be overstated, as it shapes the very fabric of the nation's landscape. However, this geographic richness comes hand-in-hand with vulnerability, and nowhere is this more apparent than in the relentless grip of riverbank erosion. Chairman Ghat, in Noakhali, epitomizes the struggle against this natural force, making it a focal point for understanding and addressing the profound impact of riverbank erosion in the region. The primary objective of this research work is to assessing river bank erosion & accretion and examine its the potential impact in the chairman Ghat area. In order to achieve this goal, this study employs remote sensing and GIS techniques to assess the erosion and accretion rates of Chairman Ghat on the Noakhali coast from 2000 to 2022 and conduct household survey to examine the socioeconomic impacts of river erosion on the local population. The findings revealed that Over the past 22 years, the Meghna estuary experienced a total of 667.0 km2 of erosion and 689.1 km2 of accretion. Chairman Ghat area alone witnessed a net erosion rate of 4.2 km2 per year during the specified period, resulting in a loss of 117.7 km2 of land, while only 25.3 km2 of new land was added. From household survey it is found that the adverse effects of riverbank erosion on the local community are evident, leading to the loss of vital agricultural lands, homesteads, and property damage that cannot be easily recovered. Consequently, a significant portion of the affected population is compelled to migrate, thereby exacerbating their socio-economic plight as they are forced to change professions. This research sheds light on the extent of riverbank erosion in Chairman Ghat, Noakhali, and elucidates its profound socioeconomic implications on the lives of the local inhabitants. The findings of the study will help to enhance our understanding of the specific impact of river erosion enabling the development of targeted strategies to protect vulnerable communities' lives, livelihoods, and environments. It will also promote cohesive coastal zone management, implementing robust measures that counteract the destructive consequences of riverbank erosion, thereby ensuring the sustainable development and resilience of these crucial ecosystems.